Power distribution network voltage fluctuation risk assessment method based on electric vehicle charging

Abstract

The invention discloses a power distribution network voltage fluctuation risk assessment method based on electric vehicle charging, and relates to the technical field of power grid operation analysis. The method comprises the following steps: establishing an electric vehicle charging and discharging model: establishing the electric vehicle charging and discharging model by comprehensively considering factors of charging demand, charging power, charging time, discharging power and battery state of the electric vehicle, and simulating charging and discharging behaviors of the electric vehicle under different working conditions, the accurate electric vehicle charging and discharging model and the power distribution network voltage model are constructed by comprehensively considering the charging demand, the driving habit and the external factors of the electric vehicle and combining the detailed topological structure and the electrical characteristics of the power distribution network, so that the prediction of the voltage fluctuation of the power distribution network after the electric vehicle is connected is more accurate; and a scientific basis is provided for planning, scheduling, operation and maintenance of the power distribution network, the problem of voltage fluctuation caused by disordered charging of the electric vehicle is effectively avoided, and the overall stability and safety of the power distribution network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation analysis technology, and in particular to a method for assessing the risk of voltage fluctuation in a distribution network based on electric vehicle charging. Background Art

[0002] First, when evaluating the impact of electric vehicle access on the power grid, traditional distribution network planning and operation often ignores the diversity and dynamics of electric vehicle charging demand, as well as the complexity of user driving habits and external factors. This leads to inaccurate predictions of distribution network voltage fluctuations after electric vehicles are connected, making it difficult to effectively guide the planning, scheduling and operation of the distribution network.

[0003] Secondly, the disorderly charging behavior of electric vehicles can easily cause voltage fluctuations in the distribution network, especially when a large number of electric vehicles are charging at the same time during peak hours, which will have a serious impact on the stability of the power grid. The existing distribution network management systems often lack optimization strategies for the charging characteristics of electric vehicles, and it is difficult to effectively avoid the occurrence of voltage fluctuation problems.

[0004] Finally, voltage fluctuations not only affect the stability of the power grid, but also directly damage the power users' electricity experience. Substandard voltage leads to damage to user equipment and power outages, which in turn reduces user satisfaction and trust in power grid services. Existing distribution network assessment and improvement methods often fail to detect and solve these problems in a timely manner, resulting in poor user experience.

[0005] Therefore, in view of the deficiencies and shortcomings of the existing technology, the patent of this invention proposes a distribution network voltage fluctuation risk assessment method based on electric vehicle charging, so as to improve the scientific nature of distribution network planning and operation, and enhance the service quality and electricity consumption experience of power grid users. Summary of the invention

[0006] The purpose of the present invention is to make up for the shortcomings of the prior art and provide a distribution network voltage fluctuation risk assessment method based on electric vehicle charging. It realizes the risk assessment of distribution network voltage fluctuation by constructing an accurate electric vehicle charging and discharging model and a distribution network voltage model, combined with voltage quality assessment indicators. It not only improves the accuracy and reliability of the prediction, but also provides a scientific basis for distribution network planning, scheduling and operation and maintenance, helps to ensure the stability of the power grid and the quality of electricity used by users, and is an effective solution to the challenges of electric vehicle charging.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for assessing the risk of voltage fluctuation in a distribution network based on electric vehicle charging, the specific steps of the assessment method are as follows:

[0008] S100, establishing an electric vehicle charging and discharging model: comprehensively considering the factors of the electric vehicle's charging demand, charging power, charging time, discharging power and battery status to establish an electric vehicle charging and discharging model, and simulate the charging and discharging behavior of the electric vehicle under different working conditions;

[0009] S200, establishing a distribution network voltage model: establishing a distribution network voltage model according to the topological structure and load conditions of the distribution network to reflect the voltage distribution of the distribution network under different operating conditions;

[0010] S300, construction of comprehensive risk assessment model: The electric vehicle charging and discharging model is closely integrated with the distribution network voltage model to build a comprehensive risk assessment model. By simulating the charging and discharging process of electric vehicles at different times and powers, the voltage fluctuations at each node of the distribution network are calculated and their impact on voltage quality is evaluated.

[0011] S400, risk assessment indicators: using voltage offset, voltage fluctuation, and harmonic content as indicators, solving the comprehensive risk assessment model and obtaining the risk assessment results;

[0012] S500, practical application verification and optimization: Practical application verification is carried out in distribution networks of different types and sizes to ensure the applicability and reliability of the assessment method. The comprehensive model is continuously optimized and improved based on the verification results to improve the adaptability and accuracy of the assessment method.

[0013] Further, the S100 includes:

[0014] S101, collecting basic parameters of the electric vehicle, obtaining data on the historical charging records of the electric vehicle, user driving habits and predicted driving needs, and preprocessing and standardizing the collected data;

[0015] S102, establishing a charging demand model according to the driving demand of the electric vehicle, user habits and external factors;

[0016] S103, establishing an equivalent circuit model according to the electrical characteristics of each component in the distribution network, and setting corresponding electrical parameters;

[0017] S104, establishing a discharge power model of the electric vehicle by considering the influence of the power demand of the drive motor, the vehicle speed, and the road conditions on the discharge power;

[0018] S105, integrating the charging demand model, the charging power model, and the discharging power model to form a complete electric vehicle charging and discharging model;

[0019] S106, verifying the electric vehicle charging and discharging model to evaluate its accuracy and reliability.

[0020] Furthermore, the S104 establishes a discharge power model of the electric vehicle, and determines the required charging amount Q according to the battery capacity, current power and driving demand of the electric vehicle. charge , the charging demand model formula is: Q charge =Q total -Q current , where: Q total is the total capacity of the battery, Q current is the current power, according to the charging demand and the maximum power limit of the charging station P max , calculate the charging power P charge , the charging power model formula is: P charge =min(P demand ,P max ), P charge =min(P dcmand ,P max ), according to the driving state of the electric vehicle and the discharge characteristics of the battery, determine the discharge power P discharge , the discharge power model formula is: P discharge =f(v,θ,SOC), where: v is the vehicle speed, θ is the battery temperature, and SOC is the battery state of charge. The charging demand model, charging power and charging time model, and discharge power model are integrated to form a unified charging and discharging model. The model is verified using historical data and simulation data to ensure the accuracy and reliability of the model.

[0021] Furthermore, the S200 includes

[0022] S201, collecting detailed topological data of the distribution network, obtaining real-time and historical load data of each node, and preprocessing the collected data;

[0023] S202, using graph theory methods to abstract the topological structure of the distribution network into a set of nodes and edges, and constructing a topological model of the distribution network;

[0024] S203, establishing an equivalent circuit model of each component in the distribution network according to its electrical characteristics, and setting corresponding electrical parameters;

[0025] S204, using a Newton-Raphson algorithm to take a topological model, an equivalent circuit model and load data of the distribution network as input, and creating a distribution network voltage model;

[0026] S205, using actual measurement data to verify the established distribution network voltage model, and optimizing and adjusting the model according to the verification result.

[0027] Furthermore, the construction of the distribution network topology model S202 abstracts each electrical node in the distribution network as a node in the graph, the node is represented by i, and the electrical line connecting two nodes in the distribution network is abstracted as an edge in the graph, the edge is represented by (i, j), and an adjacency matrix A is constructed, whose element A ij for: Calculate the degree d of each node i , the calculation formula is: Analyze the topology of the graph, identify the ring network and radial network structure in the network, classify the nodes according to their functions and characteristics, and define electrical properties for each edge, including resistance r ij , Reactance x ij , construct the branch impedance matrix Z, element Z ij The formula is: Z ij =r ij +jx ij , if there is no direct connection between i and j, then Z ij =0, verify whether the constructed topology model correctly reflects the physical connection and electrical characteristics of the distribution network.

[0028] Furthermore, the S204 uses the Newton-Raphson algorithm to establish a distribution network voltage model and sets an initial voltage for each node i in the distribution network. Construct the Jacobian matrix J, where the matrix elements J ij represents the partial derivative of the node j current with respect to the node i voltage, and calculates the injected power S at each node i i =P i +jQ i , where: P i is the active power Q i is the reactive power. The power imbalance is calculated for each node i using the formula: ΔS i =S i -S load,i , use the Newton-Raphson method to iteratively update the node voltage. The Newton-Raphson method formula is: Where: k represents the current iteration number, J ii are the diagonal elements of the Jacobian matrix. After each iteration, check whether the voltage update meets the convergence condition: Where: ∈ is the preset convergence threshold. When the convergence condition is met, the final node voltage V is output. i As a result of the distribution network voltage model.

[0029] Furthermore, the 300 includes:

[0030] S301, connecting the electric vehicle charging and discharging model as a dynamic load to the distribution network voltage model to ensure that the power change during the charging and discharging process of the electric vehicle can be reflected in the distribution network voltage model in real time;

[0031] S302, setting a time scenario and a power scenario, simulating the charging and discharging process of the electric vehicle in each simulation scenario, calculating the power change of the electric vehicle after it is connected to the distribution network and its impact on the voltage of each node of the distribution network;

[0032] S303, according to the standard, by comparing the voltage distribution data with the preset voltage quality evaluation index, evaluate the impact of electric vehicle charging and discharging on the voltage quality of the distribution network, identify the nodes and time periods that do not meet the standards, and analyze their potential risks to the stability of the power grid and the quality of power consumption of users;

[0033] S304, prepare a comprehensive risk assessment report, explain the impact and risks of electric vehicle charging and discharging on voltage quality, propose improvement measures and evaluate the effects.

[0034] Furthermore, the S400 includes:

[0035] S401, uses voltage deviation, voltage fluctuation and harmonic content as the main indicators for evaluating the voltage quality of the distribution network;

[0036] S402, obtaining time series data of voltage and current from the distribution network monitoring system, and preprocessing and standardizing the data;

[0037] S403, calculating the difference between the voltage value of each sampling point and the rated voltage to obtain the voltage offset, using the event detection algorithm to identify the voltage fluctuation event, calculating the total harmonic distortion rate, and evaluating the impact of the harmonics on the power grid;

[0038] S404, constructing a comprehensive risk assessment model, taking voltage deviation, voltage fluctuation, and harmonic content index as input variables, and using a weighted summation algorithm to perform comprehensive calculations to obtain a risk assessment result of distribution network voltage fluctuation;

[0039] S405, comparing the risk assessment result calculated by the comprehensive risk assessment model with the national standard to determine whether the voltage quality of the distribution network meets the standard.

[0040] Furthermore, the 404 uses a weighted summation algorithm for comprehensive calculation, selects voltage offset, voltage fluctuation and harmonic content as key indicators for evaluating the voltage fluctuation risk of the distribution network, obtains real-time and historical data of the indicators through calculation, performs data preprocessing, and standardizes the data, determines a weight for each indicator to reflect its importance in risk assessment, and uses a weighted summation algorithm to perform comprehensive calculation on each indicator to obtain a comprehensive risk score. The weighted summation formula is: Risk Score = w1 × Voffset +w2×V fluctuation +w3×H harmonics , where: w1, w2, w3 are the weights of voltage offset, voltage fluctuation and harmonic content respectively, V offsct ,V fluctuation ,H harmonics It is a standardized indicator value. According to the comprehensive risk score, the risk is divided into different levels, the comprehensive risk assessment results are output, and corresponding explanations and suggestions are provided. The model is verified using actual data and optimized and adjusted based on the verification results.

[0041] Furthermore, the S500 includes:

[0042] S501, determine the specific objectives of verification and optimization, and select the type and scale of the distribution network;

[0043] S502, installing the required monitoring equipment and sensors at the test point to ensure that the operation data of the distribution network can be accurately obtained;

[0044] S503, regularly collect monitoring data, use data mining methods to analyze the collected data, and evaluate the performance of the method

[0045] S504: Optimize and adjust the comprehensive risk assessment model based on the identified problems

[0046] S505, redeploy the optimized method to the test point for verification, and continue to monitor the operation data of the distribution network to ensure that the optimization effect reaches the expected result.

[0047] Compared with the existing technology, the electronic certificate user information security and privacy assessment method has the following beneficial effects:

[0048] 1. The present invention constructs an accurate electric vehicle charging and discharging model and a distribution network voltage model by comprehensively considering the charging needs, driving habits and external factors of electric vehicles, and combining the detailed topological structure and electrical characteristics of the distribution network. This not only makes the prediction of the voltage fluctuation of the distribution network after the electric vehicle is connected more accurate, but also provides a scientific basis for the planning, scheduling and operation and maintenance of the distribution network, effectively avoiding the voltage fluctuation problem caused by the disorderly charging of electric vehicles, and improving the overall stability and safety of the distribution network.

[0049] 2. Through detailed voltage quality assessment, the present invention can timely discover and identify nodes and time periods in the distribution network where voltage does not meet standards due to charging and discharging of electric vehicles, and then provide targeted improvement measures for power grid managers. It not only helps to quickly restore and improve voltage quality and ensure the stable operation of the power grid, but also significantly improves the electricity users' electricity experience, reduces equipment damage or power outages caused by voltage fluctuations, and enhances electricity users' satisfaction and trust in power grid services.

[0050] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 The flowchart of a voltage fluctuation risk assessment method for distribution network based on electric vehicle charging is shown in the figure. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Embodiment 1

[0055] A distribution network voltage fluctuation risk assessment method based on electric vehicle charging, the assessment method comprises the following steps: establishing an electric vehicle charging and discharging model, establishing a distribution network voltage model, constructing a comprehensive risk assessment model, risk assessment indicators, and actual application verification and optimization.

[0056] First, enter the electric vehicle charging and discharging model establishment stage (S100), collect basic parameters of the electric vehicle, such as battery capacity and motor efficiency, obtain historical charging records from charging stations, including charging time and charging amount, collect user driving habit data through the vehicle information system, and predict driving needs, clean, denoise and standardize all collected data, consider user driving needs, charging habits and external factors such as weather conditions and traffic, establish a charging demand model, establish an equivalent circuit model based on the electrical characteristics of the distribution network, set electrical parameters to reflect the actual grid characteristics, analyze the power demand of the drive motor, establish a model to predict the discharge power under different conditions, integrate the charging demand model, the equivalent circuit model with the charging time model and the discharge power model to form a comprehensive model that can simulate the charging and discharging behavior of electric vehicles under different conditions, and use actual data to verify the comprehensive model to ensure the accuracy of the model prediction.

[0057] Next, enter the distribution network voltage model establishment stage (S200), collect the topological structure data of the distribution network and the real-time and historical load data of each node, and perform preprocessing. Use graph theory methods to abstract the topological structure of the distribution network into a set of nodes and edges, build a topological model, use the preprocessed data to establish an equivalent circuit model of the distribution network, and set electrical parameters. Apply the Newton-Raphson algorithm to take the topological model, equivalent circuit model and load data of the distribution network as input to create a distribution network voltage model. Use actual measurement data to verify the established voltage model, and optimize and adjust according to the results.

[0058] Subsequently, the comprehensive risk assessment model construction phase (S300) is entered, and the electric vehicle charging and discharging model is connected to the distribution network voltage model as a dynamic load. Different time scenarios and power scenarios are set to simulate the charging and discharging process of the electric vehicle, and the impact on the voltage of each node in the distribution network is calculated. According to the voltage quality assessment standard, the simulated voltage distribution data is compared, the nodes and time periods that do not meet the standards are identified, and the potential risks are analyzed. A risk assessment report is compiled, improvement measures are proposed, and their potential effects are evaluated.

[0059] Then, enter the risk assessment index stage (S400), determine voltage offset, voltage fluctuation and harmonic content as evaluation indicators, obtain voltage and current data from the monitoring system, preprocess and standardize, calculate voltage offset, identify voltage fluctuation events, and evaluate harmonic impact. Build a risk assessment model, use the weighted summation method to calculate the risk assessment results, compare the assessment results with national standards, and judge whether the voltage quality meets the standards.

[0060] Finally, enter the actual application verification and optimization stage (S500), clarify the verification and optimization goals, select the appropriate distribution network for testing, install monitoring equipment at key nodes of the distribution network, collect operation data, regularly collect and analyze data, evaluate the performance of the method, and optimize and adjust the risk assessment model based on the analysis results, redeploy the optimized method, continue to monitor the operation data, and ensure the optimization effect.

[0061] By implementing the steps of the above embodiment, the impact of electric vehicle charging on the voltage fluctuation of the distribution network is evaluated, and corresponding risk assessment and improvement measures are proposed.

[0062] Embodiment 2:

[0063] This embodiment particularly emphasizes the establishment of electric vehicle charging and discharging models and distribution network voltage models:

[0064] Collect basic parameters of electric vehicles, including battery capacity, historical charging records, user driving habits and predicted driving needs, pre-process and standardize the data, and determine the required charging amount Q based on the battery capacity, current power and driving needs of the electric vehicle. charge , the formula for creating the charging demand model is: Q charge =Q total -Q current , according to the charging demand and the maximum power limit of the charging station P max , calculate the charging power P charge , the charging power model formula is: P charge =min9P demand ,P max ), P charge =min(P dcmand ,P max ), the discharge power model determines the discharge power P according to the driving state of the electric vehicle and the discharge characteristics of the battery discharge , the discharge power model formula is: P discharge =f(v,θ,SOC).

[0065] Integrate the charging demand model, charging power and discharging power model to form a unified charging and discharging model. Use historical data and simulation data to verify the charging and discharging model to ensure the accuracy and reliability of the model.

[0066] Collect the topological structure data of the distribution network and the real-time and historical load data of each node, and perform preprocessing. Abstract each electrical node in the distribution network as a node in the graph and calculate the degree d of each node. i , the calculation formula is: Analyze the topological structure of the graph, identify the ring network and radial network structure in the network, classify the nodes according to their functions and characteristics, define electrical properties for each edge, and verify whether the constructed topological model correctly reflects the physical connection and electrical characteristics of the distribution network.

[0067] Use the Newton-Raphson algorithm to establish the distribution network voltage model, set the initial voltage for each node, construct the Jacobian matrix J, and calculate the injected power P inj =P active +jQ rcactive , update the node voltage V (k+1) =V (k) -J -1 .S (k) , check whether the voltage update meets the convergence conditions: When the convergence condition is met, the final node voltage V is output i As a result, the distribution network voltage model is verified using actual measurement data, and optimization adjustments are made based on the results.

[0068] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for assessing the risk of voltage fluctuation in a distribution network based on electric vehicle charging, characterized in that: The specific steps of this evaluation method are: S100, establishing an electric vehicle charging and discharging model: comprehensively considering the factors of the electric vehicle's charging demand, charging power, charging time, discharging power and battery status to establish an electric vehicle charging and discharging model, and simulate the charging and discharging behavior of the electric vehicle under different working conditions; S200, establishing a distribution network voltage model: establishing a distribution network voltage model according to the topological structure and load conditions of the distribution network to reflect the voltage distribution of the distribution network under different operating conditions; S300, construction of comprehensive risk assessment model: The electric vehicle charging and discharging model is closely integrated with the distribution network voltage model to build a comprehensive risk assessment model. By simulating the charging and discharging process of electric vehicles at different times and powers, the voltage fluctuations at each node of the distribution network are calculated and their impact on voltage quality is evaluated. S400, risk assessment indicators: using voltage offset, voltage fluctuation, and harmonic content as indicators, solving the comprehensive risk assessment model and obtaining the risk assessment results; S500, practical application verification and optimization: Practical application verification is carried out in distribution networks of different types and sizes to ensure the applicability and reliability of the assessment method. The comprehensive model is continuously optimized and improved based on the verification results to improve the adaptability and accuracy of the assessment method.

2. According to claim 1, a method for assessing the risk of voltage fluctuation in a distribution network based on electric vehicle charging, characterized in that: The S100 includes: S101, collecting basic parameters of the electric vehicle, obtaining data on the historical charging records of the electric vehicle, user driving habits and predicted driving needs, and preprocessing and standardizing the collected data; S102, establishing a charging demand model according to the driving demand of the electric vehicle, user habits and external factors; S103, establishing an equivalent circuit model according to the electrical characteristics of each component in the distribution network, and setting corresponding electrical parameters; S104, establishing a discharge power model of the electric vehicle by considering the influence of the power demand of the drive motor, the vehicle speed, and the road conditions on the discharge power; S105, integrating the charging demand model, the charging power model, and the discharging power model to form a complete electric vehicle charging and discharging model; S106, verifying the electric vehicle charging and discharging model to evaluate its accuracy and reliability.

3. According to claim 2, a method for assessing the risk of voltage fluctuation in a distribution network based on electric vehicle charging, characterized in that: The S104 establishes a discharge power model for the electric vehicle, and determines the required charging amount Q according to the battery capacity, current power and driving demand of the electric vehicle. charge , the formula for creating the charging demand model is: Q charge =Q total -Q current , where: Q total is the total capacity of the battery, Q current is the current power, according to the charging demand and the maximum power limit of the charging station P max , calculate the charging power P charge , the charging power model formula is: P charge =min(P demand ,P max ), P charge =min(P dcmand ,P max ), according to the driving state of the electric vehicle and the discharge characteristics of the battery, determine the discharge power P discharge , the discharge power model formula is: P discharge =f(v,θ,SOC), where: v is the vehicle speed, θ is the battery temperature, and SOC is the battery state of charge. The charging demand model, charging power and charging time model, and discharge power model are integrated to form a unified charging and discharging model. The model is verified using historical data and simulation data to ensure the accuracy and reliability of the model.

4. According to claim 1, a method for assessing the risk of voltage fluctuation in a distribution network based on electric vehicle charging, characterized in that: The S200 includes S201, collecting detailed topological data of the distribution network, obtaining real-time and historical load data of each node, and preprocessing the collected data; S202, using graph theory methods to abstract the topological structure of the distribution network into a set of nodes and edges, and constructing a topological model of the distribution network; S203, establishing an equivalent circuit model of each component in the distribution network according to its electrical characteristics, and setting corresponding electrical parameters; S204, using a Newton-Raphson algorithm to take a topological model, an equivalent circuit model and load data of the distribution network as input, and creating a distribution network voltage model; S205, using actual measurement data to verify the established distribution network voltage model, and optimizing and adjusting the model according to the verification result.

5. According to claim 4, a method for assessing the risk of voltage fluctuation in a distribution network based on electric vehicle charging is characterized in that: The construction of the distribution network topology model S202 abstracts each electrical node in the distribution network as a node in the graph, and the node is represented by i. The electrical line connecting two nodes in the distribution network is abstracted as an edge in the graph, and the edge is represented by (i, j). An adjacency matrix A is constructed, and its element A ij for: Calculate the degree d of each node i , the calculation formula is: Analyze the topology of the graph, identify the ring network and radial network structure in the network, classify the nodes according to their functions and characteristics, and define electrical properties for each edge, including resistance r ij , Reactance x ij , construct the branch impedance matrix Z, element Z ij The formula is: Z ij =r ij +jx ij , if there is no direct connection between i and j, then Z ij =0, verify whether the constructed topology model correctly reflects the physical connection and electrical characteristics of the distribution network.

6. According to claim 4, a method for assessing the risk of voltage fluctuation in a distribution network based on electric vehicle charging is characterized in that: S204 uses the Newton-Raphson algorithm to establish a distribution network voltage model to set an initial voltage for each node i in the distribution network Construct the Jacobian matrix J, where the matrix elements J ij represents the partial derivative of the node j current with respect to the node i voltage, and calculates the injected power S at each node i i =P i +jQ i , where: P i is the active power Q i is the reactive power. The power imbalance is calculated for each node i using the formula: ΔS i =S i -S load,i , use the Newton-Raphson method to iteratively update the node voltage. The Newton-Raphson method formula is: Where: k represents the current iteration number, J ii are the diagonal elements of the Jacobian matrix. After each iteration, check whether the voltage update meets the convergence condition: Where: ∈ is the preset convergence threshold. When the convergence condition is met, the final node voltage V is output. i As a result of the distribution network voltage model.

7. According to claim 1, a method for assessing the risk of voltage fluctuation in a distribution network based on electric vehicle charging, characterized in that: The 300 includes: S301, connecting the electric vehicle charging and discharging model as a dynamic load to the distribution network voltage model to ensure that the power change during the charging and discharging process of the electric vehicle can be reflected in the distribution network voltage model in real time; S302, setting a time scenario and a power scenario, simulating the charging and discharging process of the electric vehicle in each simulation scenario, calculating the power change of the electric vehicle after it is connected to the distribution network and its impact on the voltage of each node of the distribution network; S303, according to the standard, by comparing the voltage distribution data with the preset voltage quality evaluation index, evaluate the impact of electric vehicle charging and discharging on the voltage quality of the distribution network, identify the nodes and time periods that do not meet the standards, and analyze their potential risks to the stability of the power grid and the quality of power consumption of users; S304, prepare a comprehensive risk assessment report, explain the impact and risks of electric vehicle charging and discharging on voltage quality, propose improvement measures and evaluate the effects.

8. According to claim 1, a method for assessing the risk of voltage fluctuation in a distribution network based on electric vehicle charging, characterized in that: The S400 includes: S401, uses voltage deviation, voltage fluctuation and harmonic content as the main indicators for evaluating the voltage quality of the distribution network; S402, obtaining time series data of voltage and current from the distribution network monitoring system, and preprocessing and standardizing the data; S403, calculating the difference between the voltage value of each sampling point and the rated voltage to obtain the voltage offset, using the event detection algorithm to identify the voltage fluctuation event, calculating the total harmonic distortion rate, and evaluating the impact of the harmonics on the power grid; S404, constructing a comprehensive risk assessment model, taking voltage deviation, voltage fluctuation, and harmonic content index as input variables, and using a weighted summation algorithm to perform comprehensive calculations to obtain a risk assessment result of distribution network voltage fluctuation; S405, comparing the risk assessment result calculated by the comprehensive risk assessment model with the national standard to determine whether the voltage quality of the distribution network meets the standard.

9. A method for assessing the risk of voltage fluctuation in a distribution network based on electric vehicle charging according to claim 8, characterized in that: The 404 uses a weighted summation algorithm to perform comprehensive calculations, selects voltage offset, voltage fluctuation and harmonic content as key indicators for evaluating the voltage fluctuation risk of the distribution network, obtains real-time and historical data of the indicators through calculation, performs data preprocessing, and standardizes the data, determines a weight for each indicator to reflect its importance in risk assessment, and uses a weighted summation algorithm to perform comprehensive calculations on each indicator to obtain a comprehensive risk score. The weighted summation formula is: Risk Score = w1 × V offset +w2×V fluctuation +w3×H harmonics , where: w1, w2, w3 are the weights of voltage offset, voltage fluctuation and harmonic content respectively, V offsct ,V fluctuation ,H harmonics It is a standardized indicator value. According to the comprehensive risk score, the risk is divided into different levels, the comprehensive risk assessment results are output, and corresponding explanations and suggestions are provided. The model is verified using actual data and optimized and adjusted based on the verification results.

10. The method for assessing the risk of voltage fluctuation in a distribution network based on electric vehicle charging according to claim 1, characterized in that: The S500 includes: S501, determine the specific objectives of verification and optimization, and select the type and scale of the distribution network; S502, installing the required monitoring equipment and sensors at the test point to ensure that the operation data of the distribution network can be accurately obtained; S503, regularly collect monitoring data, use data mining methods to analyze the collected data, and evaluate the performance of the method; S504, optimizing and adjusting the construction of the comprehensive risk assessment model based on the identified problems; S505, redeploy the optimized method to the test point for verification, and continue to monitor the operation data of the distribution network to ensure that the optimization effect reaches the expected result.

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